A robotic arm maneuvers a teat preparation cup and executes instructions from a robotic arm controller. The controller comprises an interface, a memory, and a processor. The processor instructs the sensor to perform a first scan. If the first scan discovers a first set of teats, the processor moves the robotic arm a first distance and instructs the sensor to perform a second scan. If the second scan discovers a second set of teats, the processor moves the robotic arm to a location under the first teat, and instructs the sensor to perform a third scan. The processor determines if the third scan discovers a third set of teats. If each of the first set, second set, and third set of discovered teats comprises the first teat, the processor instructs the robotic arm to attach the preparation cup to the first teat.

A robotic arm maneuvers a teat preparation cup and executes instructions from a robotic arm controller. The controller comprises an interface, a memory, and a processor. The processor instructs the sensor to perform a first scan. If the first scan discovers a first set of teats, the processor moves the robotic arm a first distance and instructs the sensor to perform a second scan. If the second scan discovers a second set of teats, the processor moves the robotic arm to a location under the first teat, and instructs the sensor to perform a third scan. The processor determines if the third scan discovers a third set of teats. If each of the first set, second set, and third set of discovered teats comprises the first teat, the processor instructs the robotic arm to attach the preparation cup to the first teat.

A system comprising a teat preparation cup coupled to a vacuum pump. A first pressure sensor measures the vacuum level of the vacuum pump. A second pressure sensor measures vacuum level inside the preparation cup. The system further comprises a controller comprising an interface, a memory, and a processor.

The processor determines if the vacuum pump has sufficient vacuum level and communicates instructions to induce a partial vacuum in the teat preparation cup. The processor determines if the teat preparation cup has a vacuum level greater than a vacuum threshold value and when this condition is achieved, communicates instructions to initiate a teat preparation cycle comprising allowing fluid flowing from a fluid source container through the teat preparation cup into a fluid disposal container.

A system comprising a teat preparation cup coupled to a vacuum pump. A first pressure sensor measures the vacuum level of the vacuum pump. A second pressure sensor measures vacuum level inside the preparation cup. The system further comprises a controller comprising an interface, a memory, and a processor.

The processor determines if the vacuum pump has sufficient vacuum level and communicates instructions to induce a partial vacuum in the teat preparation cup. The processor determines if the teat preparation cup has a vacuum level greater than a vacuum threshold value and when this condition is achieved, communicates instructions to initiate a teat preparation cycle comprising allowing fluid flowing from a fluid source container through the teat preparation cup into a fluid disposal container.

Systems and methods for detecting udder disease based on machine learning methods and complementary supporting techniques are presented. Included are methods for assembling time sequences of images of each animal of a herd or set for subsequent use in per-animal image analysis for disease detection. Methods presented also include image pre-processing methods used prior to image analysis, resulting in contrast and resolution optimization such as appropriate image intensity level adjustment and resolution downsampling for more rapid and more accurate disease detection. Combinatorial techniques for compositing whole-udder images or udder-quarter images from partial images captures are described. Methods are provided for power usage optimization in regard to computing resources used in the computing-intensive AI analysis methods. Location-based and animal history-based detection refinements are incorporated into described systems. Further presented are methods for multi-modal and multi-factor detection of udder disease, as well as methods for infection type classification.

Systems and methods for detecting udder disease based on machine learning methods and complementary supporting techniques are presented. Included are methods for assembling time sequences of images of each animal of a herd or set for subsequent use in per-animal image analysis for disease detection. Methods presented also include image pre-processing methods used prior to image analysis, resulting in contrast and resolution optimization such as appropriate image intensity level adjustment and resolution downsampling for more rapid and more accurate disease detection. Combinatorial techniques for compositing whole-udder images or udder-quarter images from partial images captures are described. Methods are provided for power usage optimization in regard to computing resources used in the computing-intensive AI analysis methods. Location-based and animal history-based detection refinements are incorporated into described systems. Further presented are methods for multi-modal and multi-factor detection of udder disease, as well as methods for infection type classification.

A system and method for monitoring at least one operating pressure in a milking installation by a pressure sensor measuring values of a pressure level in a component of a milking point of the milking installation, the pressure level being indicative of the at least one operating pressure to be monitored. A processing node generates monitoring data representing a series of measured values of the pressure level, and the monitoring data contains temporal indicators designating a respective timestamp indicative of a point in time when a value of the pressure level was measured. The temporal indicators serve as a basis for triggering at least one alarm, such as when a timestamp indicates that the pressure level was measured to a value outside of an acceptable range of values at the point in time indicated by the timestamp.

A system and method for monitoring at least one operating pressure in a milking installation by a pressure sensor measuring values of a pressure level in a component of a milking point of the milking installation, the pressure level being indicative of the at least one operating pressure to be monitored. A processing node generates monitoring data representing a series of measured values of the pressure level, and the monitoring data contains temporal indicators designating a respective timestamp indicative of a point in time when a value of the pressure level was measured. The temporal indicators serve as a basis for triggering at least one alarm, such as when a timestamp indicates that the pressure level was measured to a value outside of an acceptable range of values at the point in time indicated by the timestamp.

Disclosed is a monitoring system, and associated method, relating to the operation of a dairy plant, the system being adapted or able to monitor one or more of the following criteria: the mixing and/or dispensing of animal health products, the mixing and/or dispensing of teat treatments, the mixing and/or dispensing of chemicals, the mixing and/or dispensing of cleaning chemicals, the monitoring of plant cleaning operations, the usage of plant products/treatments/chemicals, and/or any other aspects associated with the operation of a dairy plant. The system is preferably able to monitor a dispensing system associated with a dairy plant.

Disclosed is a monitoring system, and associated method, relating to the operation of a dairy plant, the system being adapted or able to monitor one or more of the following criteria: the mixing and/or dispensing of animal health products, the mixing and/or dispensing of teat treatments, the mixing and/or dispensing of chemicals, the mixing and/or dispensing of cleaning chemicals, the monitoring of plant cleaning operations, the usage of plant products/treatments/chemicals, and/or any other aspects associated with the operation of a dairy plant. The system is preferably able to monitor a dispensing system associated with a dairy plant.